The folks in the front of the room stared with a forced intensity at (what must have been) the 23rd straight slide showing data about website performance. Their glazed eyes would have been entirely evident if the speaker wasn’t so intently focused on pointing out the change in bounce rate between August and July. In the back of the room, Brian wasn’t able to summon the energy to care. The gentle hum of laptops, dim lighting, and endless onslaught of data practically begged his mind to wander...

Specificity is the soul of narrative

This is a frequently-repeated lesson from John Hodgman's excellent podcast Judge John Hodgman. His fake Internet courtroom demands that its litigants share specific information and stories to bring their arguments to life.

Unfortunately, this lesson is often lost when people use data to communicate. Which is not to confuse detail for specificity. Detail — at least in the data communication context — simply means the access to more and more granular data. Specificity requires something more: delivering information that is familiar to your audience, letting them connect with the subject matter at a more personal level. The data is no longer an abstraction, it is something tangible and real.

How do we deliver more specificity in our data stories? Here are three ideas:

Remind your audience of the people behind the data

Begin with an individual story

Explore individual patterns and behaviors

1. Remind your audience that we are talking about individual people or things.

Data is an imperfect reflection of activity in the real world. You want to find ways to emphasize the connection between real people and the data points shown on the screen. A few examples:

Use icons as a subtle reminder that we are talking about people

Use images of people to humanize the data

Use individual components (people) to compose the visualizations. A tradition bar chart is transformed into a stack of the individual units.

In one memorable meeting, I was demonstrating our workforce analytics solution to a prospective client. I was showing the distribution visualization (above) and was careful to roll over individual people to help explain its meaning. As I was highlighting an employee with 40 years of experience at their company, an executive burst out: “Wait a second, that woman was my elementary school teacher.” The data came to life for him that day.

2. Begin with individual stories before showing the big picture.

One of the all-time best specificity-is-the-soul-of-narrative visualizations is the Gun Deaths visual created by Periscope. Take a moment to experience it.

To create emotional impact from the data, the designer starts this visual by showing one gun death at a time.

Gradually the animation speeds up until the viewer understands the terrifying weight of the many lives cut short.

Your data story may be on a more banal topic, but there are still ways to show the individual stories. What does a prototypical conversion in your sales pipeline look like? What is the financial impact of an individual patient going to an abnormally expensive healthcare provider?

3. Provide your audience with the ability to dive into many individual patterns and behaviors.

One compelling anecdote may hook your reader; the ability to see many stories can provide a powerful tool for analysis.

A long time ago we introduced the concept of customer flashcards — visualizations that tell the story of individual people or things, create a language for reading behavior patterns, and the opportunity to flip through many of these visuals. Finding patterns doesn’t have to be the exclusive domain of machine learning — as humans, we are pretty good at seeing and interpreting patterns ourselves.

Here’s an example from a project we did to see patterns of online learning. Once we found an effective way to show how students took courses, we quickly identified common behaviors that would have been lost in the typical summarization of data.

Data storytelling is still finding its fundamental principles and discovering how effectively impact readers. Bringing specificity into these data stories may just be a bedrock principle that we can adopt from a wise Internet judge.

The Data Storytelling Revolution is coming to the K-12 Education world -- in its own unique way. Two days at the annual National Center for Education Statistics STATS DC Data Conference in Washington DC gave me an up-close view of how education leaders were using data to drive policy and understanding school performance. This insiders view was thanks to an invitation by our partners at the Public Consulting Group, one of the leading education consulting practices in the country.

After attending a handful of presentations and hanging out with industry experts, here are a few of my impressions:

Education leaders have a fresh energy about data visualization and data storytelling.

To start with, the conference was subtitled: “Visualizing the Future of Education through Data”. To back this up, the program featured more than a dozen presentations about how to present data to make an impact. There was good-natured laughing and self-flagellation about poor visualizations, and oooh's and aaah's at good visualizations. There was also a genuine appreciation for how important it is to “bridge the last mile” of data to reach important audiences.

Unsurprisingly, Educators understand the need to reach and teach their data audiences.

For many of the attendees, their most important data audiences (teachers, parents, school administrators) are relative novices when it comes to interpreting data. There was a general appreciation that finding better ways to communicate of their data was paramount. The old ways of delivering long reports and clunky dashboards wasn’t going to suffice. The presenters emphasized “less is more” and the value of well-written explanations. I even ran into a solution vendor committed to building data fluency among teachers. This sincere sensitivity to the needs of the audience isn’t always so prevalent in other industries.

Data technologies and tools take a backseat to process, people, and politics.

On August 20th and 21st, I’ll see you at the Nashville Analytics Summit. When I do, I bet we’ll be surrounded by vendors and wide-eyed attendees talking about big data, machine learning, and artificial intelligence. Not in the Education world. After the lessons of No Child Left Behind and years of stalled and misguided data initiatives, Education knows that successful use of data starts with:

Getting people to buy-in to the meaning, purpose, and value of the data;

Establishing consistent processes for collecting reliable data;

Navigating the political landmines required to move their projects forward.

The Education industry is more focused on building confidence in data, than in performing high-wire analytical acts.

Education has not yet found the balance between directed data stories and flexible guidance.

I sat in on a presentation by the Education Department where they shared a journalism-style data story that revealed insights about English Learners. There website was the first in a series of public explorations of their treasure-trove of data.

On the other extreme, the NCES shared a reporting-building engine for navigating another important data set. On one extreme, a one-off static data story; on the other, a self-service report generation tool. The future is in the middle — purposeful, guided analysis complemented by customization to serve each individual viewer. The Education industry is still finding their way toward this balance.

Every industry needs to find its own path to better use of data. It was enlightening for me to see how a portion of the K12 Education industry is evolving on this journey.

Putting data on a screen is easy. Making it meaningful is so much harder. Gathering a collection of visualizations and calling it a data story is easy (and inaccurate). Making data-driven narrative that influences people...hard.

Here are 25 more lessons we've learned (the hard way) about what's easy and what's hard when it comes to telling data stories:

Easy: Picking a good visualization to answer a data questionHard: Discovering the core message of your data story that will move your audience to action

Easy: Knowing who is your target audienceHard: Knowing what motivates your target audience at a personal level by understanding their everyday frustrations and career goals

The Nashville Analytics Summit will be on us before we know it. This special gathering of data and analytics professionals is scheduled for August 20th and 21st, and should be bigger and better than ever. From my first experience with the Summit in 2014, it has consistently been a highlight of my year. My first Summit took place at the Lipscomb Spark Center meeting space with about a hundred attendees. Just a few years later, we'd grown to more than 450 attendees and moved into the Omni Hotel.

Mark it on your calendar. I'll give you five reasons why it is a can't-miss event if you work with data:

We've invited world-renowned keynote speakers like Stephen Few and Thomas Davenport. You won't believe who we are planning to bring in this year.

There isn't a better networking event for analytics professionals in our region. Whether you're looking for talent or looking for the next step in your career, you'll meet kindred spirits, data lovers, and innovative businesses. For two years in a row, we have hired Juice interns directly from conversations at the Summit.

It's for everyone who works with data. Analyst, Chief Data Officer, or Data Scientist... we've got you covered. There are technical workshops and presentations for the hands-on practitioner and case studies and management strategies for the executive. We're committed to bringing you quality and diverse content.

It's a "Goldilocks" conference. Some conferences go on for days. Some conferences are a sea of people, or too small to expand your horizons. The Analytics Summit is two days, 500-something people, and conveniently located in the cosy confines of the Omni Hotel. It is easy to meet new people and connect with people you know.

See what's happening. Nashville has a core of companies committed to building a special and innovative analytics community. We have innovators like Digital Reasoning, Stratasan, and Juice Analytics. We have larger companies making a deep commitment to analytics like Asurion, HCA, and Nissan. The Summit is the best chance to see the state of our thriving analytics community.

Now that you're convinced you can't miss out, you're may wonder what to do next. First, block out your calendar (August 20 and 21). Next, find a colleague who you'd like to go with. Want to be even more involved? We invited dozens of local professionals to speak at the Summit. You can submit a proposal to present.

Finally, if you don't want your company to miss out on the opportunity to reach our entire analytics community, there are still slots for sponsors.

“Data Monetization is a hot topic because it has two words that everyone loves. We all love data, and who doesn’t want to monetize something?”

These were the words that kicked off the 2018 Data Monetization Workshop to a roomful of attendees and industry experts who had gathered to discuss the question that followed this observation: what does Data Monetization actually mean?

This question was discussed at length over the course of the half-day event and was the impetus for speaker topics related to using data for social good, how to account for data on a balance sheet, how AI will affect the future of Data Monetization, and more. Here are some of the most important themes and takeaways from the discussions of the day.

What Is Data Monetization?

Data monetization is about data value, not data dollars. It’s not about selling customer lists, but about deriving value.

Data Monetization encompasses business intelligence and takes a much broader perspective on what can be done with data. Analyzing what options exist outside the enterprise, what products and services can be created using data, and trying to get data into the hands of decision-makers are all components of Data Monetization.

Data for Good

Most organizations aren’t trying to sell your personal data; they’re focused on using information to improve city performance, prevent mass shootings, and rescue people from sex trafficking.

Nobody owns data. Companies and organizations have rights to data, but in order for progress to be made data must be shared and communicated.

The Dark Side of Data

While data offers many beneficial opportunities, there also exists a dark side of data. What complications does something like what happened with Cambridge Analytica have on future opportunities for Data Monetization?

Using certain data is not always a question of “Is this legal?” but rather “Is this ethical?” Sometimes data is available but not right to use, which can feel like a restraint at times but leads to being an organization being perceived as trustworthy. It is important to have a solid core philosophy on what data you do and don’t use before it becomes necessary to bring in lawyers and PR teams.

Poor data literacy is seen across the board. If you don’t read the fine print, you can sign your data rights away. Many problems with the use of personal data are often due to mismatched expectations.

People don’t always understand how valuable data is and what an asset they hold. You have to teach people to think in technicolor. Some companies try to exclude information, but more information changes the landscape and provides more context.

Creating data products with different derivations is one way to communicate data to different roles (e.g., an analyst versus a CEO). You have to meet people where they are.

Being transparent with a product roadmap is a great way to demonstrate to people that data products will look different as time goes on. Users can know what features they can expect and when.

Doing Things Differently and Looking to the Future

There are emerging technologies that can help make processes easier. Right now you just have to ask yourself, “How can I do things a little bit differently today?”

Doug Laney Is One Cool Dude

Doug Laney was kind enough to join us remotely from his vacation to answer audience questions about his book Infonomics -- of which every audience member got a free copy!

Special thanks to all of the speakers, to MapR for sponsoring the post-workshop networking reception, and to everyone that attended! If you have questions or comments about the Data Monetization Workshop, feel free to reach out to info@juiceanalytics.com.

Over the years, we’ve had the pleasure to work with many great individuals and companies and through our work have gained the ability to sympathize with their experiences of what we like to call “going from 0 to 100."

No, we’re not endorsing excessive speeding in your car. We’re talking about going from having nothing but hopes and dreams about delivering engaging analytics (0) to having an interactive data story that your users don’t want to put down (100).

Because we’ve focused our efforts on taking clients from 0 to 100, commonalities or trends for best practices in the data and design experience (read: everything between 1 and 99) have become increasingly clear. Use these four tips to make your introduction to data products a better, more frictionless experience.

1. Know your audience

What do the end users you have in mind for the product look like? What questions will users ask of the data? What actions will they take with the answers to these questions? These are all things you should know before beginning to work on data products.

Be specific about for whom you are creating a data product. If you try to provide insights for too many types of business roles you run the risk of making it too broad for any role to gather insights from the data.

2. Gather the right data

When putting together the data to be used in your product, it’s important to discern the difference between “more data” and “more records."

More data: It’s not always in your best interest to gather the most “data” possible. By doing this, you run the risk of gathering data that you may not use and wasting money in the process.

More Records: Gathering “more records” (read: rows of data) is a better strategy as you prepare for your data product. Doing so can alleviate the effects of outliers and unearth trends in the data.

3. If you’re new to the data, begin with an MVP (Minimum Viable Product) and let your users determine what features should be included

Building out all the bells and whistles you think you might need at the beginning the data product’s life can be expensive. Starting with an MVP that is put in the hands of actual end users will help determine what data is actually needed and what design aspects are best for your purposes.

Helps with data: Starting with an MVP helps determine the shape and caveats that exist within your data, and allows your users to make decisions about what data is most important to them.

Helps with design: By starting with an MVP, all of the questions that you and your users have for the data are answered by the design. Additional features can then be added from that point on in a more cost-effective manner.

4. Be open-minded about visualizations

We won’t get into data visualization principles in this section because that warrants a totally separate article, but a simple point here: just because you saw similar data in a pie chart once doesn’t mean that is the only (or best) way to visualize your data.

Because your users are the ultimate consumers of the data, let them be the judges of what visualizations will be most effective for them.

Easy peasy, right? We think so, but maybe that’s only because we’ve helped so many customers get from 0 to 100. If you're still not sure what your next steps should be, we’re here to help. Learn more about our 0 to 100 process by checking out the document below.

The ability of an excel novice (i.e. me) to use a pivot table is basically naught. My ability to manipulate data does not exist, and yet I work for one of the most forward-thinking data presentation companies! Nevermind why I was hired, I quickly learned how to use a Juicebox application because Juicebox is designed with the everyday end user in mind. We have tackled the problem of data delivery to both analytical and non-analytical groups. In this post, I want to chat about one of the features that make that possible: connected slices. What is a slice? A slice is a Juice term for a data visualization within a section of Juicebox application.

I have mentioned before that Narrative Flow is important to Juicebox. Our applications are web-based and users expect to move and navigate from top to bottom, like when interacting with a webpage. Part of that movement from top to bottom in Juicebox means that as the user is making selections within the application, those selections should not only carry down the page but that they should also inform the visuals that follow.

We strive to be the world's best platform for telling data stories and because of that connecting our visuals together is vital. When someone makes a selection in the topmost slice, it places a filter on the data and the selection they make. This filter helps the user narrow down their selection and drill into the data.

Much of the problems with static reports and dashboards is that they only give the user a top-level view of his or her data. Traditional solutions do not provide the ability to drill further to discover what factors could be driving the data. In essence, today's charts, dashboards, reports, and BI solutions give the user a snapshot and not the whole story.

Curious to see what else is included in Juicebox? Check out some of these posts highlighting other unique features:

It’s a predicament that we’ve seen many times over: your data is stuck. You’ve tried some reporting through some Excel pivot tables, or you’ve messed around with a Tableau trial, but felt like there wasn’t enough engaging content to get your users excited. Rationalizing why you can’t get your data to be impactful for your business, you think things like, “maybe my users are talking about the data but I just don’t know about it” or “maybe the data isn’t structured in a way that allows for valuable insights to be extracted from it."

If you’re sitting there thinking that your mind is being read by our artificial intelligence, you’re wrong. It's because at Juice we have seen this scenario played out too many times and we’ve made it our mission to make these issues a thing of the past. What you need to do is give your data a jumpstart.

Here’s our suggested plan of action for getting your data unstuck and giving it the jumpstart it needs:

1. Get your data into a readable structure.

The first row of your data should always represent the column’s title

Columns should contain the same type of values, respectively

Each row should represent a case or a single instance within the data and should contain a date of when that data was collected. This means that two different rows in the data can represent the same entity with data collected for it at different points in time.

As a consequence of the rule above, the data should include a row identifier column that can be repeated to indicate that different rows of data are representing the same entities.

Make yourself a metadata sheet (also commonly known as “data definitions”) that you and other users of the data can refer to.

Give your audience a call-to-action, let them know why the data is important and why they should care.

Begin with presenting high-level key metrics. Think about what the most important numbers are you to your intended audience(s).

Give your audience the option to select a few different categories in which to segment and parse-out those important numbers. Doing this will allow your audience to drill-down in the data to get from a high-level to a granular level.

Allow your audience to take the data they have drilled down to with them. This could be one row of data out of the thousands they started with at the high-level.

3. Engaging your audience in data discussions

Sounds like a good plan of action, right? If you're still not sure what your next steps should be, we’re here to help.

We’ll work with you to get your data in a structure that makes it valuable, or even create data for you. We’ll build you a data story with that data that helps you and your users understand the data so that you can turn data insights into business actions. We’ll get your users engaged in data discussions and app design feedback so that you know they’re engaged with the data and you know how valuable they perceive the app to be. So drop us a line, we’re here to help.

It can be a challenging climb to reshape how people think about solving problems. We encounter this challenge daily as we work to build the best solution for communicating data the world has ever seen. We operate in an arena where good-enough solutions — Excel, PowerPoint, and other visual analytics tools — have left people with deeply-rooted habits and a blasé acceptance of the status quo. That’s not good enough for us, and it isn’t good enough for these six companies that are rethinking how business tools should work:

Slack is the current king-of-the-hill for shaking up the status quo. Sure, we had email, file sharing, and messaging apps before Slack, but we didn’t have single, elegant tool for team collaboration.

What’s cool about it?Slack made integrations easy from the start. We use everything from ChatOps with our development team to HeyTaco for everyday appreciation of our colleagues. Slack's approach to 'channels' found the right balance for open communication by topic.

I only recently stumbled across the excellent visualizations available through Flourish. There are many, many tools for putting data visualizations on a screen; few vendors are so obviously passionate about their craft.

What’s cool about it?Flourish is more than another charting library — they are making world-class visualizations accessible. I was particularly impressed by the clever use of animation in those visualizations. At Juice, we appreciate that new users won’t always be able to read a visualization without some guidance. Animation can help draw a user’s attention to the most important information right off the bat.

Kialo is “a debate platform powered by reason.” It cuts through the noise of social and online media by removing the worst parts of debating online (trolls, fake statistics, unrelated cat gifs) while strengthening the best.

What’s cool about it?Kialo creates a structured dialogue with visualization, voting, and commenting. Whether discussing politics or the merits of a new project, Kialo has focused on an overlooked need: a place other than the comments section to examine arguments and consider new viewpoints.

Typeform is "the versatile data collection tool for professionals." It's a thoroughly modern survey-building solution that I’ve enjoyed using for over a year.

What’s cool about it?Typeform's survey-authoring interface is remarkably intuitive. Adding questions, structuring logical flows, and navigating your survey is silky smooth. Similarly, the end-user experience is beautifully designed with selectors and animations that make it (almost) fun to fill out a survey.

From the founder of SlideRocket, Beautiful AI is a next-generation solution for creating web-based presentations. They say all you have to do is "think of an idea, choose a template, and get to work."

What’s cool about it?Beautiful AI has taken a giant leap past a tool like Google Slides. It comes with a collection of smart slide layout templates. Better yet, these slide layouts automatically update as you add more content. The tool also comes with an easy-to-use integration with third-party image libraries so you can incorporate pictures into your presentation.

It may not be nice to hear, but deep down you know it's true. You can see it in the way that data gets delivered to audiences: email attachments no one wants to open, 50-page slide decks filled with never-ending complex charts, and scrolling pages of dashboards with no context around them. It's not only messy, it interrupts the ability to adequately share and communicate important information about data.

So what's the solution? How do we deliver data to audience where they can draw out conclusions and information that is going to be meaningful to them? The answer: data fluency.

Data fluency, or data literacy, is something that we at Juice have been talking about for years (we literally wrote the book on it). We recently sat down with Dalton Ruer, or as he's more familiarly known around the web, QlikDork, to discuss the details of data fluency and how to achieve it. Check out the video below to hear from Juice CEO Zach Gemignani and Global Head of Data Literacy at Qlik Jordan Morrow and learn what having data literate consumers means, how to get good at choosing visualizations and weaving them into engaging stories, what a data fluent culture looks like, and so much more.